This course introduces the planning, design, and implementation of decision support systems (DSS) and expert systems (ES). Problem sets, case studies, and journal articles are used to examine topics such as end-user computing, the evaluation and selection of DSS generators and ES shells, group support systems, and neural network. Students gain hands-on experience using DSS generators, prototyping languages and ES shells.
Upon completion of this course, students will be able to:
Specified on the course schedule/outline
|W||—||Withdrawal during weeks 1 - 7|
|WF||—||Withdrawal failing after week 7|
|NF||—||Failing – Not actively engaged|
For more details about the Grading System, please see the current catalog.
Students must be actively engaged in the course. For a definition of active engagement, please see the current catalog.
Cheating and plagiarism are serious offenses against the University’s academic integrity and are consequently strictly prohibited. All students must familiarize themselves with the University policy on Academic Integrity.
Penalties for cheating and plagiarism are described in the University policy on Academic Integrity in the catalog. They include failure of the assignment, failure for the course, or dismissal from the University. For the complete Cheating/Plagiarism policy, please see the current catalog.
Students who have disabilities that may impact their performance in this course should follow the process described under the heading Accommodations for the Disabled in the current catalog.
Date of last review: Unknown